Hybrid Machine Learning
Hybrid Machine Learning is an approach that combines multiple machine learning techniques, models, or paradigms to leverage their complementary strengths and overcome individual limitations. It often integrates symbolic AI (e.g., rule-based systems) with statistical or deep learning methods, or merges different algorithms like neural networks with decision trees. This methodology aims to improve performance, interpretability, robustness, and adaptability in complex real-world applications.
Developers should learn and use Hybrid Machine Learning when building systems that require both high accuracy and explainability, such as in healthcare diagnostics, financial fraud detection, or autonomous vehicles, where pure black-box models may be insufficient. It is particularly valuable in scenarios with limited labeled data, as it can incorporate domain knowledge through symbolic components, or when dealing with heterogeneous data types that benefit from diverse modeling approaches.